Deep Learning for Intelligent Identification of Arrhythmias (ECG-LEARNING)
Deep Learning for Intelligent Identification of Arrhythmias (ECG-LEARNING): an Investigator-initiated, National Multicenter, Retrospective-prospective, Cohort Study
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Guoliang Li, M.D.
- Phone Number: +8613759982523
- Email: liguoliang_med@163.com
Study Contact Backup
- Name: Chaofeng Sun, M.D.
- Email: cfsun1@mail.xjtu.edu.cn
Study Locations
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Shaanxi
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Xi'an, Shaanxi, China, 710061
- First Affiliated Hospital of Xi'an Jiantong University
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- For retrospective study: 1.Patients with arrhythmia diagnosed by routine surface 12-lead electrocardiogram or Holter; 2.The type of arrhythmia is diagnosed by intracardiac electrophysiological examination.
- For prospective study: 1.Patients with arrhythmia diagnosed by routine surface 12-lead electrocardiogram or Holter; 2.Intracardiac electrophysiological examination is planned.
Exclusion Criteria:
- Lack of routine surface 12-lead electrocardiogram or holter data;
- Lack of intracardiac electrophysiological examination;
- Patients refused to sign informed consent and refused to participate in the study.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Experimental Group
ECG data and clinical data from this group of arrhythmia patients will be used to build a deep learning model.
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No interventions will be given to patients.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
A deep learning model designed to intelligently identify the types of arrhythmia.
Time Frame: 1 day after the enrollment.
|
The model is trained on the training set, the best model and hyperparameters are selected through the verification set, and finally the model results are tested on the test set.
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1 day after the enrollment.
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The sensitivity, specificity and accuracy of the deep learning model
Time Frame: 1 day after the enrollment.
|
The sensitivity, specificity and accuracy of a deep learning model designed were evaluated by intracardiac electrophysiological examination results to identify patients with arrhythmia from various centers.
|
1 day after the enrollment.
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Guoliang Li, M.D., First Affiliated Hospital Xi'an Jiaotong University
Study record dates
Study Major Dates
Study Start (Estimated)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- XJTU1AF2023LSK-170
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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